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Overadjustment bias and unnecessary adjustment in epidemiologic studies.
Open accessAbstract
Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.
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“To ensure that we did not improperly adjust for mediating variables, 22 we used covariates measured at wave 1 instead of wave 2.”
From Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. (2019) · cites this paper as [22]
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“Adjustment for an intermediate variable such as absenteeism can provide valid estimates of a direct causal effect of chronic health conditions on low school performance under certain assumptions, namely, the absence of unmeasured confounding in the associations between absenteeism or chronic health conditions and low…”
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“Chemosphere 2005; 61(3): 383-8 [25] Schisterman EF, Cole SR, Platt RW.”
BackgroundHostinar, C. E., Lachman, M. E., Mroczek, D. K., Seeman, T. E., & Miller, G. E. (2015) · Developmental Psychology · doi:10.1037/dev0000049 · Cited by 167
Nondepolarizing Neuromuscular Blocking Agents, Reversal, and Risk of Postoperative Pneumonia
Bulka, C. M., Terekhov, M. A., Martin, B. J., Dmochowski, R. R., Hayes, R. M., & Ehrenfeld, J. M. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001279 · Cited by 165
Mirrahimi, A., de Souza, R. J., Chiavaroli, L., Sievenpiper, J. L., Beyene, J., Hanley, A. J., Augustin, L. S. A., Kendall, C. W. C., & Jenkins, D. J. A. (2012) · Journal of the American Heart Association · doi:10.1161/jaha.112.000752 · Cited by 161
“Another inherent limitation of observational analyses is the potential problem for residual confounding as well as the possibility of overadjusting, which remains an area of debate in epidemiology.(63) Perhaps, the most common limitation in meta-analyses of dietary studies is the combination of dietary data collected…”
BackgroundMatched designs and causal diagrams
Mansournia, M. A., Hernán, M. Á., & Greenland, S. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt083 · Cited by 161
Are socio-economic disparities in diet quality explained by diet cost?
Monsivais, P., Aggarwal, A., & Drewnowski, A. (2010) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2010.122333 · Cited by 169
“A mediating variable, by definition, explains at least some of the observed association between an exposure (eg, indicators of SEP) and an outcome (eg, nutrient density of the diet),(44) and, when controlled for statistically through adjustment (shown in figure 2), biases the main exposureeoutcome association towards…”
BackgroundHo, F. K. W., Gray, S. R., Welsh, P. I., Petermann-Rocha, F. E., Foster, H. M. E., Waddell, H., Anderson, J. J., Lyall, D. M., Sattar, N. A., Gill, J. M. R., et al. (2020) · BMJ · doi:10.1136/bmj.m688 · Cited by 161
McDowell, P. J., McDowell, R., Busby, J., Eastwood, M. C., Patel, P. H., Jackson, D. J., Mansur, A. H., Patel, M., Burhan, H., Doe, S. J., et al. (2023) · European Respiratory Journal · doi:10.1183/13993003.00819-2023 · Cited by 160
“We chose this limited set of adjustment variables to prevent overadjustment bias, in which adjustment is made for variables that lie on the causal path between the exposure and outcome [19, 20].”
MethodMediterranean diet and mobility decline in older persons
Milaneschi, Y., Bandinelli, S., Corsi, A. M., Lauretani, F., Paolisso, G., Dominguez, L. J., Semba, R. D., Tanaka, T., Abbatecola, A. M., Talegawkar, S. A., et al. (2010) · Experimental Gerontology · doi:10.1016/j.exger.2010.11.030 · Cited by 156
“This adjustment was not included in previous analyses in order to avoid overadjustment, defined as the control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome [24].”
BackgroundMethodOpioid use for noncancer pain and risk of myocardial infarction amongst adults
Li, L., Setoguchi, S., Cabral, H., & Jick, S. S. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12035 · Cited by 157
Evaluating the Use of Nonrandomized Real‐World Data Analyses for Regulatory Decision Making
Franklin, J. M., Glynn, R. J., Martin, D., & Schneeweiß, S. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1351 · Cited by 157
“As in RWE studies with internal control groups, the identification of external control groups should also ensure that covariates are measured prior to the start of exposure to an existing treatment to avoid adjusting for causal intermediaries.(35) Finally, design of the external control group should acknowledge the im…”
BackgroundRisk Factors for Young-Onset Dementia in the UK Biobank
Hendriks, S., Ranson, J. M., Peetoom, K. K. B., Lourida, I., Tai, X. Y., de Vugt, M. E., Llewellyn, D. J., & Köhler, S. (2023) · JAMA Neurology · doi:10.1001/jamaneurol.2023.4929 · Cited by 162
Farmer, R. E., Mathur, R., Schmidt, A. F., Bhaskaran, K., Fatemifar, G., Eastwood, S. V., Finan, C., Denaxas, S., Smeeth, L., & Chaturvedi, N. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.118.011638 · Cited by 144
“While blood pressure and cholesterol are strong predictors of CVD incidence, it is likely that these 2 variables are at least partly on the causal pathway between body composition and the outcomes or share common risk factors with our main exposures, making adjustment for them as “confounders” inappropriate and potent…”
MethodJohns, L. E., Ferguson, K. K., Soldin, O. P., Cantonwine, D. E., Rivera-González, L. O., Toro, L. V. A. D., Calafat, A. M., Ye, X., Alshawabkeh, A. N., Cordero, J. F., et al. (2015) · Reproductive Biology and Endocrinology · doi:10.1186/1477-7827-13-4 · Cited by 158
“However, we have no reason to expect that these would be associated with phthalate exposure, so any deficiencies in these substances would affect the precision of the effect estimates rather than the effect estimates themselves [76].”
BackgroundRace, APOL1 Risk, and eGFR Decline in the General Population
Grams, M. E., Rebholz, C. M., Chen, Y., Rawlings, A. M., Estrella, M. M., Selvin, E., Appel, L. J., Tin, A., & Coresh, J. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2015070763 · Cited by 152
Song, X., Jousilahti, P., Stehouwer, C. D. A., Söderberg, S., Onat, A., Laatikainen, T. K., Yudkin, J. S., Dankner, R. S., Morris, R. W., Tuomilehto, J., et al. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.203 · Cited by 156
Endocrine disrupting chemicals and endometriosis
Smarr, M. M., Kannan, K., & Louis, G. M. B. (2016) · Fertility and Sterility · doi:10.1016/j.fertnstert.2016.06.034 · Cited by 149
Duchowny, K. A., Ackley, S. F., Brenowitz, W. D., Wang, J., Zimmerman, S. C., Caunca, M. R., & Glymour, M. M. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.18314 · Cited by 154
The health consequences of child marriage: a systematic review of the evidence
Fan, S., & Koski, A. (2022) · BMC Public Health · doi:10.1186/s12889-022-12707-x · Cited by 219
Irvin, M. R., Booth, J. N., Shimbo, D., Lackland, D. T., Oparil, S., Howard, G., Safford, M. M., Muntner, P., & Calhoun, D. A. (2014) · Journal of the American Society of Hypertension · doi:10.1016/j.jash.2014.03.003 · Cited by 146
van Sloten, T. T., Sigurdsson, S. O., van Buchem, M. A., Phillips, C. L., Jónsson, P. V., Ding, J., Schram, M. T., Harris, T. B., Gudnason, V. G., & Launer, L. J. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14050578 · Cited by 146
“We did not adjust the results for total disease loadbecause total disease load includes the load per investigated region (as indicated by the composite scores) and, thus, can be considered an overadjustment (32).”
BackgroundMothers’ Adverse Childhood Experiences and Their Young Children’s Development
Sun, J., Patel, F., Jacobs, R. R., Frank, D. A., Black, M. M., & Chilton, M. M. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2017.07.015 · Cited by 149
Vasunilashorn, S. M., Dillon, S. T., Inouye, S. K., Ngo, L. H., Fong, T. G., Jones, R. N., Travison, T. G., Schmitt, E. M., Alsop, D. C., Freedman, S. D., et al. (2017) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14913 · Cited by 145
“Charlson Comorbidity Index, n (%) 0 257 (46) 54 (40) 203 (48) 1 139 (25) 23 (17) 116 (27) 2 97 (17) 38 (29) 59 (14) ≥3 67 (12) 19 (14) 48 (11) Vascular comorbidity, n (%) 214 (38) 60 (45) 154 (36)”
"Toward a Clearer Definition of Confounding" Revisited With Directed Acyclic Graphs
Howards, P. P., Schisterman, E. F., Poole, C., Kaufman, J. S., & Weinberg, C. R. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws127 · Cited by 143
Self-citationChiu, Y., Afeiche, M. C., Gaskins, A. J., Williams, P. L., Petrozza, J. C., Tanrikut, C., Hauser, R. B., & Chavarro, J. E. (2015) · Human Reproduction · doi:10.1093/humrep/dev064 · Cited by 143
“Specifically, abstinence time was included regardless of statistical significance since this is a well-known predictor of most semen quality parameters thus helping to reduce the amount of unexplained random variability in the model (Schisterman et al., 2009).”
BackgroundLauritzen, H. B., Larose, T. L., Øien, T., Sandanger, T. M., Odland, J. Ø., van de Bor, M., & Jacobsen, G. W. (2018) · Environmental Health · doi:10.1186/s12940-017-0338-x · Cited by 142
“As adjustment for a mediator may introduce collider bias if there are shared unmeasured causes of both the mediator (SGA status) and the outcome (childhood overweight) [36], we did not include prenatal growth or SGA status in the multivariate analyses.”
BackgroundAssociations between historical redlining and birth outcomes from 2006 through 2015 in California
Nardone, A. L., Casey, J. A., Rudolph, K. E., Karasek, D. A., Mujahid, M. S., & Morello-Frosch, R. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237241 · Cited by 154
Adversity exposure during sensitive periods predicts accelerated epigenetic aging in children
Marini, S., Davis, K. A., Soare, T. W., Zhu, Y., Suderman, M. J., Simpkin, A. J., Smith, A. D. A. C., Wolf, E. J., Relton, C. L., & Dunn, E. C. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.104484 · Cited by 145
Pesticide use and incident diabetes among wives of farmers in the Agricultural Health Study
Starling, A. P., Umbach, D. M., Kamel, F., Long, S., Sandler, D. P., & Hoppin, J. A. (2014) · Occupational and Environmental Medicine · doi:10.1136/oemed-2013-101659 · Cited by 138
“In this scenario, adjustment for body mass index could bias the effect estimate, typically (though not always) toward the null[28].”
Background
Metadata
- Authors
- Schisterman, E. F., Cole, S. R., & Platt, R. W.
- Affiliations
- National Institutes of Health (US)
- University of North Carolina at Chapel Hill (US)
- McGill University (CA)
- Corresponding author
- Schisterman, Enrique F.
- Venue
- Epidemiology
- Published
- 2009-07
- Volume
- 20
- Issue
- 4
- Pages
- 488-495
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC2744485
- OpenAlex
- W2004722883
- Cited by
- 2,096
- References
- 39
- Citations by year
- 2026: 298 · 2025: 160 · 2024: 123 · 2023: 153 · 2022: 172 · 2021: 156 · 2020: 167 · 2019: 154 · 2018: 117 · 2017: 130 · 2016: 129 · 2015: 98 · 2014: 78 · 2013: 60 · 2012: 38
- FWCI
- 27.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (R03AI071763)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (Z01HD008761)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- intermediate variables, confounding, selection bias, causal graphs, maternal smoking, neonatal mortality, regression adjustment, stratification, sample size
- MeSH terms
- Female, Humans, Infant Mortality, Infant, Newborn, Population Dynamics, Smoking, Models, Statistical, Epidemiologic Research Design, Bias, Causality
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